Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100959
The Lancet Digital Health
{"title":"Evidence and responsibility of artificial intelligence use in mental health care","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100959","DOIUrl":"10.1016/j.landig.2025.100959","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100959"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient certainty for some patients but not for others, raising concerns about fairness. To address this limitation, the effective sample size has been proposed as a measure of sampling uncertainty. We developed a computational method to estimate effective sample sizes for a wide range of prediction models, including machine learning approaches. In this Viewpoint, we illustrated the approach using a clinical dataset (N=23 034) across five model types: logistic regression, elastic net, XGBoost, neural network, and random forest. During simulations, our approach generated accurate estimates of effective sample sizes for logistic regression and elastic net models, with minor deviations noted for the other three models. Although model performance metrics were similar across models, substantial differences in effective sample sizes and risk predictions were observed among patients in the clinical dataset. In conclusion, prediction uncertainty at the individual prediction level can be substantial even when models are developed using large samples. Effective sample size is thus a promising measure to communicate the uncertainty of predicted risk to individual users of machine learning-based prediction models.
{"title":"Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models","authors":"Doranne Thomassen PhD , Toby Hackmann MSc , Prof Jelle Goeman PhD , Prof Ewout Steyerberg PhD , Prof Saskia le Cessie PhD","doi":"10.1016/j.landig.2025.100911","DOIUrl":"10.1016/j.landig.2025.100911","url":null,"abstract":"<div><div>Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient certainty for some patients but not for others, raising concerns about fairness. To address this limitation, the effective sample size has been proposed as a measure of sampling uncertainty. We developed a computational method to estimate effective sample sizes for a wide range of prediction models, including machine learning approaches. In this Viewpoint, we illustrated the approach using a clinical dataset (N=23 034) across five model types: logistic regression, elastic net, XGBoost, neural network, and random forest. During simulations, our approach generated accurate estimates of effective sample sizes for logistic regression and elastic net models, with minor deviations noted for the other three models. Although model performance metrics were similar across models, substantial differences in effective sample sizes and risk predictions were observed among patients in the clinical dataset. In conclusion, prediction uncertainty at the individual prediction level can be substantial even when models are developed using large samples. Effective sample size is thus a promising measure to communicate the uncertainty of predicted risk to individual users of machine learning-based prediction models.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100911"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145641240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100944
Federica Miglietta , Maria Vittoria Dieci
{"title":"Artificial intelligence and tumour-infiltrating lymphocytes in breast cancer: bridging innovation and feasibility towards clinical utility","authors":"Federica Miglietta , Maria Vittoria Dieci","doi":"10.1016/j.landig.2025.100944","DOIUrl":"10.1016/j.landig.2025.100944","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100944"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100908
Alexandra J Zimmer PhD , Rishav Das MSc , Patricia Espinoza Lopez MD , Vaidehi Nafade MSc , Genevieve Gore MLIS , César Ugarte-Gil PhD , Prof Kian Fan Chung MD , Woo-Jung Song PhD , Prof Madhukar Pai PhD , Simon Grandjean Lapierre MD
Quantifying cough can offer value for respiratory disease assessment and monitoring. Traditionally, patient-reported outcomes have provided subjective insights into symptoms. Novel digital cough counting tools now enable objective assessments; however, their integration into clinical practice is limited. The aim of this scoping review was to address this gap in the literature by examining the use of automated and semiautomated cough counting tools in patient care and public health. A systematic search of six databases and preprint servers identified studies published up to Feb 12, 2025. From 6968 records found, 618 full-text articles were assessed for eligibility, and 77 were included. Five clinical use cases were identified—disease diagnosis, severity assessment, treatment monitoring, health outcome prediction, and syndromic surveillance—with scarce available evidence supporting each use case. Moderate correlations were found between objective cough frequency and patient-reported cough severity (median correlation coefficient of 0.42, IQR 0·38 to 0·59) and quality of life (median correlation coefficient of −0·49, −0·63 to −0·44), indicating a complex relationship between quantifiable measures and perceived symptoms. Feasibility challenges include device obtrusiveness, monitoring adherence, and addressing patient privacy concerns. Comprehensive studies are needed to validate these technologies in real-world settings and show their clinical value. Early feasibility and acceptability assessments are essential for successful integration.
{"title":"Objective cough counting in clinical practice and public health: a scoping review","authors":"Alexandra J Zimmer PhD , Rishav Das MSc , Patricia Espinoza Lopez MD , Vaidehi Nafade MSc , Genevieve Gore MLIS , César Ugarte-Gil PhD , Prof Kian Fan Chung MD , Woo-Jung Song PhD , Prof Madhukar Pai PhD , Simon Grandjean Lapierre MD","doi":"10.1016/j.landig.2025.100908","DOIUrl":"10.1016/j.landig.2025.100908","url":null,"abstract":"<div><div>Quantifying cough can offer value for respiratory disease assessment and monitoring. Traditionally, patient-reported outcomes have provided subjective insights into symptoms. Novel digital cough counting tools now enable objective assessments; however, their integration into clinical practice is limited. The aim of this scoping review was to address this gap in the literature by examining the use of automated and semiautomated cough counting tools in patient care and public health. A systematic search of six databases and preprint servers identified studies published up to Feb 12, 2025. From 6968 records found, 618 full-text articles were assessed for eligibility, and 77 were included. Five clinical use cases were identified—disease diagnosis, severity assessment, treatment monitoring, health outcome prediction, and syndromic surveillance—with scarce available evidence supporting each use case. Moderate correlations were found between objective cough frequency and patient-reported cough severity (median correlation coefficient of 0.42, IQR 0·38 to 0·59) and quality of life (median correlation coefficient of −0·49, −0·63 to −0·44), indicating a complex relationship between quantifiable measures and perceived symptoms. Feasibility challenges include device obtrusiveness, monitoring adherence, and addressing patient privacy concerns. Comprehensive studies are needed to validate these technologies in real-world settings and show their clinical value. Early feasibility and acceptability assessments are essential for successful integration.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100908"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145582684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100924
Arman Koul MS , Deborah Duran PhD , Tina Hernandez-Boussard PhD
In the evolving landscape of artificial intelligence (AI), the assumption that more data lead to better models has driven unchecked reliance on synthetic data to augment training datasets. Although synthetic data address crucial shortages of real-world training data, their overuse might propagate biases, accelerate model degradation, and compromise generalisability across populations. A concerning consequence of the rapid adoption of synthetic data in medical AI is the emergence of synthetic trust—an unwarranted confidence in models trained on artificially generated datasets that fail to preserve clinical validity or demographic realities. In this Viewpoint, we advocate for caution in using synthetic data to train clinical algorithms. We propose actionable safeguards for synthetic medical AI, including standards for training data, fragility testing during development, and deployment disclosures for synthetic origins to ensure end-to-end accountability. These safeguards uphold data integrity and fairness in clinical applications using synthetic data, offering new standards for responsible and equitable use of synthetic data in health care.
{"title":"Synthetic data, synthetic trust: navigating data challenges in the digital revolution","authors":"Arman Koul MS , Deborah Duran PhD , Tina Hernandez-Boussard PhD","doi":"10.1016/j.landig.2025.100924","DOIUrl":"10.1016/j.landig.2025.100924","url":null,"abstract":"<div><div>In the evolving landscape of artificial intelligence (AI), the assumption that more data lead to better models has driven unchecked reliance on synthetic data to augment training datasets. Although synthetic data address crucial shortages of real-world training data, their overuse might propagate biases, accelerate model degradation, and compromise generalisability across populations. A concerning consequence of the rapid adoption of synthetic data in medical AI is the emergence of synthetic trust—an unwarranted confidence in models trained on artificially generated datasets that fail to preserve clinical validity or demographic realities. In this Viewpoint, we advocate for caution in using synthetic data to train clinical algorithms. We propose actionable safeguards for synthetic medical AI, including standards for training data, fragility testing during development, and deployment disclosures for synthetic origins to ensure end-to-end accountability. These safeguards uphold data integrity and fairness in clinical applications using synthetic data, offering new standards for responsible and equitable use of synthetic data in health care.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100924"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100912
William J Bolton PhD , Richard Wilson MPharm , Prof Mark Gilchrist MSc , Prof Pantelis Georgiou PhD , Prof Alison Holmes MD , Timothy M Rawson PhD
<div><h3>Background</h3><div>Challenges exist when translating artificial intelligence (AI)-driven clinical decision support systems (CDSSs) from research into health-care settings, particularly in infectious diseases, an area in which behaviour, culture, uncertainty, and frequent absence of a ground truth enhance the complexity of medical decision making. We aimed to evaluate clinicians’ perceptions of an AI CDSS for intravenous-to-oral antibiotic switching and how the system influences their decision making.</div></div><div><h3>Methods</h3><div>This randomised, multimethod study enrolled health-care professionals in the UK who were regularly involved in antibiotic prescribing. Participants were recruited through personal networks and the general email list of the British Infection Association. The first part of the study involved a semistructured interview about participants’ experience of antibiotic prescribing and their perception of AI. The second part used a custom web app to run a clinical vignette experiment: each of the 12 case vignettes consisted of a patient currently receiving intravenous antibiotics, and participants were asked to decide whether or not the patient was suitable for switching to oral antibiotics. Participants were assigned to receive either standard of care (SOC) information, or SOC alongside our previously developed AI-driven CDSS and its explanations, for each vignette across two groups. We assessed differences in participant choices according to the intervention they were assigned, both for each vignette and overall; evaluated the aggregate effect of the CDSS across all switching decisions; and characterised the decision diversity across participants. In the third part of the study, participants completed the system usability scale (SUS) and technology acceptance model (TAM) questionnaires to enable their opinions of the AI CDSS to be assessed.</div></div><div><h3>Findings</h3><div>59 clinicians were directly contacted or responded to recruitment emails, 42 of whom from 23 hospitals in the UK completed the study between April 23, 2024, and Aug 16, 2024. The median age of participants was 39 years (IQR 37–47), 19 (45%) were female and 23 (55%) were male, 26 (62%) were consultants and 16 (38%) were training-grade doctors, and 14 (33%) specialised in infectious diseases. Interviews revealed mixed individualisation of prescribing and uneven use of technology, alongside enthusiasm for AI, which was conditional on evidence and usability but constrained by behavioural inertia and infrastructure limitations. Case vignette completion times and many decisions were equivalent between SOC and CDSS interventions, with clinicians able to identify and ignore incorrect advice. When a statistical difference was observed, the CDSS influenced participants towards not switching (χ<sup>2</sup> 7·73, p=0·0054; logistic regression odds ratio 0·13 [95% CI 0·03–0·50]; p=0·0031). AI explanations were used only 9% of the time when available.
{"title":"The impact of artificial intelligence-driven decision support on uncertain antimicrobial prescribing: a randomised, multimethod study","authors":"William J Bolton PhD , Richard Wilson MPharm , Prof Mark Gilchrist MSc , Prof Pantelis Georgiou PhD , Prof Alison Holmes MD , Timothy M Rawson PhD","doi":"10.1016/j.landig.2025.100912","DOIUrl":"10.1016/j.landig.2025.100912","url":null,"abstract":"<div><h3>Background</h3><div>Challenges exist when translating artificial intelligence (AI)-driven clinical decision support systems (CDSSs) from research into health-care settings, particularly in infectious diseases, an area in which behaviour, culture, uncertainty, and frequent absence of a ground truth enhance the complexity of medical decision making. We aimed to evaluate clinicians’ perceptions of an AI CDSS for intravenous-to-oral antibiotic switching and how the system influences their decision making.</div></div><div><h3>Methods</h3><div>This randomised, multimethod study enrolled health-care professionals in the UK who were regularly involved in antibiotic prescribing. Participants were recruited through personal networks and the general email list of the British Infection Association. The first part of the study involved a semistructured interview about participants’ experience of antibiotic prescribing and their perception of AI. The second part used a custom web app to run a clinical vignette experiment: each of the 12 case vignettes consisted of a patient currently receiving intravenous antibiotics, and participants were asked to decide whether or not the patient was suitable for switching to oral antibiotics. Participants were assigned to receive either standard of care (SOC) information, or SOC alongside our previously developed AI-driven CDSS and its explanations, for each vignette across two groups. We assessed differences in participant choices according to the intervention they were assigned, both for each vignette and overall; evaluated the aggregate effect of the CDSS across all switching decisions; and characterised the decision diversity across participants. In the third part of the study, participants completed the system usability scale (SUS) and technology acceptance model (TAM) questionnaires to enable their opinions of the AI CDSS to be assessed.</div></div><div><h3>Findings</h3><div>59 clinicians were directly contacted or responded to recruitment emails, 42 of whom from 23 hospitals in the UK completed the study between April 23, 2024, and Aug 16, 2024. The median age of participants was 39 years (IQR 37–47), 19 (45%) were female and 23 (55%) were male, 26 (62%) were consultants and 16 (38%) were training-grade doctors, and 14 (33%) specialised in infectious diseases. Interviews revealed mixed individualisation of prescribing and uneven use of technology, alongside enthusiasm for AI, which was conditional on evidence and usability but constrained by behavioural inertia and infrastructure limitations. Case vignette completion times and many decisions were equivalent between SOC and CDSS interventions, with clinicians able to identify and ignore incorrect advice. When a statistical difference was observed, the CDSS influenced participants towards not switching (χ<sup>2</sup> 7·73, p=0·0054; logistic regression odds ratio 0·13 [95% CI 0·03–0·50]; p=0·0031). AI explanations were used only 9% of the time when available. ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100912"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100921
Yoni Schirris MSc , Rosie Voorthuis MSc , Mark Opdam BSc , Marte Liefaard MSc , Prof Gabe S Sonke PhD , Gwen Dackus PhD , Vincent de Jong PhD , Yuwei Wang MSc , Annelot Van Rossum PhD , Tessa G Steenbruggen PhD , Lars C Steggink PhD , Elisabeth G E de Vries PhD , Prof Marc van de Vijver PhD , Roberto Salgado PhD , Efstratios Gavves PhD , Prof Paul J van Diest PhD , Prof Sabine C Linn PhD , Jonas Teuwen PhD , Renee Menezes PhD , Marleen Kok PhD , Hugo M Horlings PhD
<div><h3>Background</h3><div>The density of stromal tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with triple-negative breast cancer and reflects their immune response. Computational TIL assessment has the potential to assist pathologists in this labour-intensive task, because it can be quick and reproducible. However, computational TIL assessment models heavily rely on detailed annotations and use complex deep learning pipelines that pose challenges for model iterations and clinical deployment. Here, we propose and validate a fundamentally simpler deep learning-based model that is trained in only 10 min on 100 times fewer pathologist annotations.</div></div><div><h3>Methods</h3><div>We collected whole slide images (WSIs) with TIL scores and clinical data of 2340 patients with breast cancer, including 790 patients with triple-negative breast cancer, from three cohorts in three countries (one each in the USA, UK, and Netherlands) and three randomised clinical trials in the Netherlands. Morphological features were extracted from WSIs using a pathology foundation model. Our model, label-efficient computational stromal TIL assessment (ECTIL), directly regresses the WSI TIL score from these features. We trained ECTIL on a single cohort from The Cancer Genome Atlas (n=356, ECTIL-TCGA), on only triple-negative breast cancer samples from four cohorts (n=400, ECTIL-TNBC), and on all molecular subtypes of five cohorts (n=1964, ECTIL-combined). We computed the concordance between ECTIL and the pathologist using the Pearson's correlation coefficient (<em>r</em>) and computed the area under the receiver operating characteristic curve (AUROC) using the pathologist TIL scores split into the clinically relevant TILs-high (≥30%) and TILs-low (<30%) groups. We also performed multivariate Cox regression analyses on the PARADIGM cohort with complete clinicopathological variables (n=384) to assess hazard ratios for overall survival, independent of clinicopathological factors.</div></div><div><h3>Findings</h3><div>ECTIL-TCGA showed concordance with the pathologist over five heterogeneous external cohorts (<em>r</em>=0·54–0·74, AUROC 0·80–0·94). ECTIL-TNBC showed a higher performance than ECTIL-TCGA on the PARADIGM cohort (<em>r</em> 0·64, AUROC 0·83 <em>vs r</em> 0·58, AUROC 0·80), and ECTIL-combined attained the highest concordance on an external test set (<em>r</em> 0·69, AUROC 0·85). Multivariate cox regression analyses indicated that every 10% increase of ECTIL-combined TIL scores was associated with improved overall survival (hazard ratio 0·85, 95% CI 0·77–0·93; p=0·0007), which was independent of clinicopathological variables and similar to the pathologist score (0·86, 0·81–0·92; p<0·0001).</div></div><div><h3>Interpretation</h3><div>In conclusion, our study showed that ECTIL could score TILs on haematoxylin and eosin-stained, formalin-fixed, paraffin-embedded WSIs in a single step, attaining high concordance with an expert pat
{"title":"Label-efficient computational tumour infiltrating lymphocyte assessment in breast cancer (ECTIL): multicentre validation in 2340 patients with breast cancer","authors":"Yoni Schirris MSc , Rosie Voorthuis MSc , Mark Opdam BSc , Marte Liefaard MSc , Prof Gabe S Sonke PhD , Gwen Dackus PhD , Vincent de Jong PhD , Yuwei Wang MSc , Annelot Van Rossum PhD , Tessa G Steenbruggen PhD , Lars C Steggink PhD , Elisabeth G E de Vries PhD , Prof Marc van de Vijver PhD , Roberto Salgado PhD , Efstratios Gavves PhD , Prof Paul J van Diest PhD , Prof Sabine C Linn PhD , Jonas Teuwen PhD , Renee Menezes PhD , Marleen Kok PhD , Hugo M Horlings PhD","doi":"10.1016/j.landig.2025.100921","DOIUrl":"10.1016/j.landig.2025.100921","url":null,"abstract":"<div><h3>Background</h3><div>The density of stromal tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with triple-negative breast cancer and reflects their immune response. Computational TIL assessment has the potential to assist pathologists in this labour-intensive task, because it can be quick and reproducible. However, computational TIL assessment models heavily rely on detailed annotations and use complex deep learning pipelines that pose challenges for model iterations and clinical deployment. Here, we propose and validate a fundamentally simpler deep learning-based model that is trained in only 10 min on 100 times fewer pathologist annotations.</div></div><div><h3>Methods</h3><div>We collected whole slide images (WSIs) with TIL scores and clinical data of 2340 patients with breast cancer, including 790 patients with triple-negative breast cancer, from three cohorts in three countries (one each in the USA, UK, and Netherlands) and three randomised clinical trials in the Netherlands. Morphological features were extracted from WSIs using a pathology foundation model. Our model, label-efficient computational stromal TIL assessment (ECTIL), directly regresses the WSI TIL score from these features. We trained ECTIL on a single cohort from The Cancer Genome Atlas (n=356, ECTIL-TCGA), on only triple-negative breast cancer samples from four cohorts (n=400, ECTIL-TNBC), and on all molecular subtypes of five cohorts (n=1964, ECTIL-combined). We computed the concordance between ECTIL and the pathologist using the Pearson's correlation coefficient (<em>r</em>) and computed the area under the receiver operating characteristic curve (AUROC) using the pathologist TIL scores split into the clinically relevant TILs-high (≥30%) and TILs-low (<30%) groups. We also performed multivariate Cox regression analyses on the PARADIGM cohort with complete clinicopathological variables (n=384) to assess hazard ratios for overall survival, independent of clinicopathological factors.</div></div><div><h3>Findings</h3><div>ECTIL-TCGA showed concordance with the pathologist over five heterogeneous external cohorts (<em>r</em>=0·54–0·74, AUROC 0·80–0·94). ECTIL-TNBC showed a higher performance than ECTIL-TCGA on the PARADIGM cohort (<em>r</em> 0·64, AUROC 0·83 <em>vs r</em> 0·58, AUROC 0·80), and ECTIL-combined attained the highest concordance on an external test set (<em>r</em> 0·69, AUROC 0·85). Multivariate cox regression analyses indicated that every 10% increase of ECTIL-combined TIL scores was associated with improved overall survival (hazard ratio 0·85, 95% CI 0·77–0·93; p=0·0007), which was independent of clinicopathological variables and similar to the pathologist score (0·86, 0·81–0·92; p<0·0001).</div></div><div><h3>Interpretation</h3><div>In conclusion, our study showed that ECTIL could score TILs on haematoxylin and eosin-stained, formalin-fixed, paraffin-embedded WSIs in a single step, attaining high concordance with an expert pat","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100921"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100915
Charles R Cleland , Adnan Tufail , Catherine Egan , Xiaoxuan Liu , Alastair K Denniston , Alicja Rudnicka , Christopher G Owen , Covadonga Bascaran , Matthew J Burton
{"title":"Independent and openly reported head-to-head comparative validation studies of AI medical devices: a necessary step towards safe and responsible clinical AI deployment","authors":"Charles R Cleland , Adnan Tufail , Catherine Egan , Xiaoxuan Liu , Alastair K Denniston , Alicja Rudnicka , Christopher G Owen , Covadonga Bascaran , Matthew J Burton","doi":"10.1016/j.landig.2025.100915","DOIUrl":"10.1016/j.landig.2025.100915","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100915"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-28DOI: 10.1016/j.landig.2025.100930
{"title":"Correction to Lancet Digital Health 2025; 7: 100866.","authors":"","doi":"10.1016/j.landig.2025.100930","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100930","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100930"},"PeriodicalIF":24.1,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号